关键词: 卷积神经网络, JavaScript脚本, 灰阶图像, 机器学习, Web安全

Abstract: Time and manpower have been wasted largely in the process of features extraction when JavaScript malicious code detection methods of machine learning are used, and these frequently-used methods have failed to meet the actual needs in the current information explosion. A JavaScript malicious code detecting method based on convolution neural network have been proposed in this paper. The sample data are collected through the crawler tool to obtain the benign and malicious JavaScript script code. The JavaScript samples are converted into the corresponding gray scale images, simultaneously, the image dataset is established. The image data set is trained when the convolution neural network model is constructed, so the model has obtained the ability to detect JavaScript malicious code. The experimental results show that the accuracy of the method is 98.9% for the 5, 800 JavaScript labeled images collected.

Key words: Convolutional Neural Network（CNN）, JavaScript’s scripts, grayscale image, machine learning, Web security